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NewsMaterial3 min readDisconfirmAI Editorial

Santhosh Kumar, CTO of Indian Fitness Company Cult.fit: AI Implementation Is an Organizational Empowerment Issue, Not a Tool Procurement Problem

Cult.fit CTO Santhosh Kumar described the company's three-stage path from standardized engineering use of Codex to extending AI capabilities across the entire organization through hackathons, management workshops, bi-weekly office hours, and embedded engineers. He reported outcomes including over 20 self-built applications by non-engineering teams and more than $100,000 in annual software savings.

2026-11-06 04:00 GMT+8

Cult.fit is a technology-driven fitness company in India, operating 708 fitness centers and serving nearly one million paying members. In a case study published by OpenAI, CTO Santhosh Kumar articulated a core judgment: the bottleneck for AI implementation lies not in model capabilities or tool access, but in whether the organization can enable non-technical staff to truly utilize these tools.

His advancement path consists of three stages. The first stage is standardization within the engineering department. Previously, developers used a mix of various coding agents and assistants; different tools implied different repository structures, agent instructions, skills, and development workflows, making it difficult for teams to share knowledge and reuse capabilities. Cult.fit selected Codex as the unified foundation, incorporating repository structures, skills, testing, code reviews, and feedback loops into common standards. Kumar stated, 'Once standardization is complete, we can build the entire software development lifecycle around a single platform.'

The second stage involves extending capabilities beyond engineering. Cult.fit made its company data easier for Codex to process, enabling various teams to find answers and perform routine analyses themselves—previously, even small data requests had to go through a central analyst team, creating a bottleneck. The HR team built workflows using Codex to manage performance evaluations, scheduling, feedback collection, and summarization. Non-engineering teams built over 20 internal applications in recent months.

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The third stage is organizational empowerment mechanisms. Kumar emphasized that enabling all employees to use AI cannot rely solely on issuing tool access permissions. Cult.fit’s approach was: first, host large-scale hackathons so employees could see the possibilities; then, organize one-day workshops for management, allowing leaders from various functions to build content relevant to their own work using Codex; the engineering team held office hours twice a week to help employees move ideas into production environments; and engineers were embedded in selected functional departments to provide technical and domain support.

Regarding cost savings, when the fees for internal dashboard software rose significantly, an engineer rebuilt the functionality using Codex over a weekend. Cult.fit now runs its own application, avoiding more than $100,000 in annual software costs. Kumar estimated that a social feed project previously took over a year to complete; with the new workflow, the team delivered it end-to-end in two months. In customer service, AI agents were integrated into the Sprinklr platform to handle requests such as waiving fines for missed classes, and the company reported a reduction in support costs of over 50%.

It should be noted that the above data all come from vendor case studies published by OpenAI, self-reported by Cult.fit. The 50% cost reduction does not disclose the calculation method, baseline composition, or whether it includes the costs of deploying Codex itself. The complexity and actual usage rates of the 20+ internal applications are also not explained. The comparison of shortening the social feed project from one year to two months is based on Kumar’s personal estimates rather than auditable project records. These are inherent boundaries of vendor case studies: they present only successes, not failures or total costs.

Kumar describes the next step goal as an 'autopilot operations org'—where AI takes on more support work around frontline teams—and 'trainer in your pocket'—extending personalized guidance to a scale human coaches cannot cover. His core argument can be summarized as: software has been democratized, and organizations need supporting mechanisms to let this capability truly flow, rather than stopping at the issuance of tool licenses.

Sources

  • openai.comOpenAI↗
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